Inferring intrahalo light from stellar kinematics -- A deep learning approach

TL;DR

This paper introduces a deep learning method using U-Net architecture to identify intrahalo light in galaxies by analyzing stellar kinematics, bridging simulation and observational approaches for galaxy formation studies.

Contribution

It develops a novel deep learning approach trained on simulated data to predict intrahalo light distribution from projected stellar kinematics, enhancing observational analysis capabilities.

Findings

Deep learning accurately predicts intrahalo light distribution in mock images.

Reinforced training improves prediction in central regions.

Model predictions are consistent across different spatial scales.

Abstract

Disentangling the stellar population in the central galaxy from the intrahalo light can help us shed light on the formation history of the host halo, as the properties of the stellar components are expected to retain traces of its formation history. Many approaches are adopted, depending on different physical assumptions (e.g. the light profile, chemical composition, and kinematical differences) and on whether the full six-dimensional phase-space information is known (much like in simulations) or whether one analyses projected quantities (i.e. observations). This paper paves the way for a new approach to bridge the gap between observational and simulation methods. We propose the use of projected kinematical information from stars in simulations in combination with deep learning to create a robust method for identifying intrahalo light in observational data to enhance understanding and consistency in studying the process of galaxy formation. Using a U-Net, we developed a methodology for predicting these contributions from a sample of mock images from hydrodynamical simulations to train, validate and test the network. Reinforced training (Attention U-Net) was used to improve the first results, as the innermost central regions of the mock images consistently overestimate the stellar intrahalo contribution. Our work shows that adequate training over a representative sample of mock images can lead to good predictions of the intrahalo light distribution. The model is mildly dependent on the training size and its predictions are less accurate when applied to mock images from different simulations. However, the main features (spatial scales and gradients of the stellar fractions) are recovered for all tests. While the method presented here should be considered as a proof of concept, future work is required to enable the application of the proposed model to observational data.